python – When using env.yml with conda whats the difference between dependencies and pip dependencies?

I am creating a .SH script for setting up automatically my dev environment in Azure ML, according to this:

The script looks like this:

set -e
# Requires: Python <4.0, >=3.7
# This script creates a custom conda environment and kernel based on a sample yml file.

conda env create  python=3.10
#conda env create -f env.yml

echo "Activating new conda environment"
conda activate envname
conda install -y ipykernel
echo "Installing kernel"
sudo -u azureuser -i <<'EOF'
conda activate envname
python -m ipykernel install --user --name envname --display-name "mykernelp310v2"
echo "Conda environment setup successfully."
pip install azure-ai-ml

my env looks like this:

name: p310v2

  - python=3.10
  - numpy
  - matplotlib
  - pandas
  - scikit-learn
  - pip:

When I check this document:

I am confused between the dependencies section and the pip section. For example scikit-learn I could put in dependencies but also on the pip section, so whats the deal here?

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